""" Matplotlib is a python 2D plotting library that mimics MATLAB and works well with NumPy. http://matplotlib.org/api/pyplot_summary.html API: http://matplotlib.org/api/pyplot_api.html#module-matplotlib.pyplot Tutorial: http://matplotlib.org/users/pyplot_tutorial.html Note: Differences from Pyplot Vs Pylab These are just different ways of doing the same thing; pyplot is the python plotting library on matplotlib and so is pylab, but pylab also imports numpy automatically. By default, just use pyplot and import numpy because its cleaner to not pollute namespace. """ import matplotlib as mpl # Only need for configuring default settings import matplotlib.pyplot as plt import matplotlib.patches as mpatches # for legend import numpy as np ### Default Settings for Matplotlib print mpl.matplotlib_fname() # Find configuration file 'matplotlibrc' ### Settings for Pyplot #plt.switch_backend('agg') #plt.ion() #Turn on interactive #plt.ioff() # Non-interactive def simple_plot(): """ Do a simple plot """ ### Setup Different Types of Data a = np.linspace(0, 50, 10, endpoint=False) # start, stop, num=50 (# of samples), endpoint (stop at last sample?) print a # [0, 5, 10, 15, 20, 25, 30, 35, 40, ] b = np.arange(0, 20, 2) # start, stop, step; returns evenly spaced values given the interval/step print b # [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] c = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) print c ### Set X and Y x, y = a, b ### Format Text plt.figure(figsize=(8,6), dpi=80) # Create 8*6 inch figure plt.title("Test") # Create a title plt.grid() # Display grid plt.xlabel("X axis") # Create text for X-axis plt.ylabel("Y axis") # Create text for Y-axis plt.axes() ### Configure X plt.xticks(np.arange(min(x), max(x)+1, 1.0)) # How often there are tick labels on x-axis #plt.set_xticklabels() # Set X tick labels (e.g. if you want 3.142 to be pi symbol instead) plt.xlim(0, 9) # Set how far we can see x-axis ### Configure Y plt.yticks(np.arange(min(y), max(y)+1, 2.0)) # How often there are tick labels on y-axis #plt.set_yticklabels() # Set Y tick lables (e.g. if you want 3.142 to be pi symbol instead) plt.ylim(0, ) # Set how far we can see y-axis ### Legend plt.legend(loc='upper left') #plt.legend(bbox_to_anchor=(1,1), loc=2) ### Plot plt.plot(x, y, color='blue', linewidth=2.5, linestyle='-', label='first') plt.plot(x, c, color='red', linewidth=2.5, linestyle='--', label='second') ### TODO: Scatterplots, Bar Plots, Pie charts #plt.scatter(x, y, alpha=0.5) # Create Scatterplot ### TODO: Annotate specific points - point out neat stuff #plt.annotate('Neat text here', xy=(0, 1), xycoords='data', # xytext=(-50, 30), textcoords='offset points', arrowprops=dict(arrowstyle="->")) ### TODO: Tick Locators - control the position of the ticks plt.savefig('my_picture.png', dpi=72) # Save image plt.show() # Display on screen def simple_subplot(): """ How to generate subplots. Subplots can be arranged like this: https://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#subplots If you want the placement of a smaller plot into a larger plot at any location, then use 'Axes' """ ### Make some data x1 = np.linspace(0.0, 5.0) x2 = np.linspace(0.0, 2.0) y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) y2 = np.cos(2 * np.pi * x2) ### First plot (top) plt.subplot(2, 1, 1) # (grid rows, grid columns, grid pos for new axes) plt.plot(x1, y1, 'ko-') plt.title('A tale of 2 subplots') plt.ylabel('Damped oscillation') ### Second plot (bottom) plt.subplot(2, 1, 2) # (grid rows, grid columns, grid pos for new axes) plt.plot(x2, y2, 'r.-') plt.xlabel('time (s)') plt.ylabel('Undamped') plt.show() # Display plot if __name__ == '__main__': simple_plot() # Do a basic plot simple_subplot() # Have multiple plots